Bridging the Human Resource Gap in Primary Health Care Delivery Systems of Developing Countries With mHealth: Narrative Literature Review
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) has the potential to solve human resource issues in the health care sector. mHealth is of particular interest in developing countries, where widespread mobile networks and access to devices are connecting people like never before. OBJECTIVE: The aim of this paper was to review published and unpublished literature, field projects, and pilot studies on mHealth usage in overcoming shortage of human health resources in developing countries. METHODS: A narrative literature review was undertaken using an iterative approach in extracting literature focused on mHealth and human health resources of low-income countries, especially India. The present review has undertaken comprehensive coverage of the work on related field projects that have been either published, accepted for publication, or pilot tested. RESULTS: This review presented the use of mHealth across various dimensions of primary health care, including data collection, disease surveillance, health education, supervision, monitoring, and feedback. Field studies of fast, error-free data collection and transmission using mHealth were also documented. New apps for supervision, monitoring, and utilization of innovative health education tools were documented in the current review. Practical limitations of mHealth and challenges set forth in developing countries included issues of data security, cost constraints, health provider privacy, and technical barriers. CONCLUSIONS: In the present review, we have documented a few mHealth projects that contribute to the proficient use of human resources. These projects pave the path for the efficient utilization of mHealth, offering solutions to emerging human resource challenges and simultaneously revamping the health care delivery in resource-limited settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".